Age-fitness pareto optimization
Age-fitness pareto optimization
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DOI：
10.1145/1830483.1830584
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发表时间：
2010-07
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影响因子：
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通讯作者：
Michael D. Schmidt;Hod Lipson
中科院分区：
文献类型：
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作者：
Michael D. Schmidt;Hod Lipson
我们提出了一个多目标的方法，避免早熟收敛的进化算法，并证明了三倍的性能改进可比的方法。先前的研究表明，将进化的种群划分为年龄组可以大大提高识别全局最优解的能力，避免收敛到局部最优解。在这里，我们建议将年龄作为一个明确的优化标准，可以进一步提高性能，更少的算法实现参数。所提出的方法在二维帕累托前沿上进化种群，包括（a）基因型在种群中存在多久（年龄）;以及（B）其性能（适应度）。我们比较这种方法与以前的方法上的符号回归问题，横扫问题的难度在一系列的解决方案的复杂性和变量的数量。我们的研究结果表明，多目标的方法确定准确的目标解决方案更经常的年龄分层的人口和标准的人口方法。多目标方法在更高复杂度的问题和更高维的数据集上也表现得更好--用更少的计算工作量找到全局最优解。
We propose a multi-objective method for avoiding premature convergence in evolutionary algorithms, and demonstrate a three-fold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion can increase performance even further, with fewer algorithm implementation parameters. The proposed method evolves a population on the two-dimensional Pareto front comprising (a) how long the genotype has been in the population (age); and (b) its performance (fitness). We compare this approach with previous approaches on the Symbolic Regression problem, sweeping the problem difficulty over a range of solution complexities and number of variables. Our results indicate that the multi-objective approach identifies the exact target solution more often that the age-layered population and standard population methods. The multi-objective method also performs better on higher complexity problems and higher dimensional datasets -- finding global optima with less computational effort.